class_docstr: Retrieves most similar terms for a given term under the hypothesis that the similarities between
  distinct terms are uniform.
  
  Parameters
  ----------
  dictionary : :class:`~gensim.corpora.dictionary.Dictionary`
      A dictionary that specifies the considered terms.
  term_similarity : float, optional
      The uniform similarity between distinct terms.
  
  See Also
  --------
  :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix`
      A sparse term similarity matrix built using a term similarity index.
  
  Notes
  -----
  This class is mainly intended for testing SparseTermSimilarityMatrix and other classes that
  depend on the TermSimilarityIndex.
code: def __init__(self, dictionary, term_similarity=0.5):
      self.dictionary = sorted(dictionary.items())
      self.term_similarity = term_similarity
  
imports: from array import array
  from itertools import chain
  import logging
  from math import sqrt
  import numpy
  from scipy import sparse
  from gensim.matutils import corpus2csc
  from gensim.utils import SaveLoad, is_corpus
label_desc: Use image node_id 1 to create a new UniformTermSimilarityIndex object from inherited base classes: TermSimilarityIndex with example: obj = UniformTermSimilarityIndex(dictionary, term_similarity)